{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UHDYi7CZgdRB"
      },
      "source": [
        "## Описание датасета\n",
        "Потребление мяса было важной частью рациона человека на протяжении тысячелетий, обеспечивая богатый источник белка и питательных веществ.Однако количество и виды потребляемого мяса сильно различались в разных культурах и исторических периодах.Чтобы лучше понять особенности потребления мяса потребление мяса во всем мире с течением времени, был составлен набор данных, который включает информацию об историческом потреблении мяса в различных регионах и странах."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "VVmNO_gLglGy",
        "outputId": "e610e126-c056-4b96-86fa-f2361bfb6a53"
      },
      "outputs": [
        {
          "data": {
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Локация</th>\n",
              "      <th>Индикатор</th>\n",
              "      <th>Тип мяса</th>\n",
              "      <th>Количество</th>\n",
              "      <th>Категория</th>\n",
              "      <th>Год</th>\n",
              "      <th>Значение</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AUS</td>\n",
              "      <td>MEATCONSUMP</td>\n",
              "      <td>BEEF</td>\n",
              "      <td>KG_CAP</td>\n",
              "      <td>A</td>\n",
              "      <td>1990</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>AUS</td>\n",
              "      <td>MEATCONSUMP</td>\n",
              "      <td>BEEF</td>\n",
              "      <td>KG_CAP</td>\n",
              "      <td>A</td>\n",
              "      <td>1991</td>\n",
              "      <td>27.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>AUS</td>\n",
              "      <td>MEATCONSUMP</td>\n",
              "      <td>BEEF</td>\n",
              "      <td>KG_CAP</td>\n",
              "      <td>A</td>\n",
              "      <td>1992</td>\n",
              "      <td>26.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AUS</td>\n",
              "      <td>MEATCONSUMP</td>\n",
              "      <td>BEEF</td>\n",
              "      <td>KG_CAP</td>\n",
              "      <td>A</td>\n",
              "      <td>1993</td>\n",
              "      <td>26.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>AUS</td>\n",
              "      <td>MEATCONSUMP</td>\n",
              "      <td>BEEF</td>\n",
              "      <td>KG_CAP</td>\n",
              "      <td>A</td>\n",
              "      <td>1994</td>\n",
              "      <td>25.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12135</th>\n",
              "      <td>EU27</td>\n",
              "      <td>MEATCONSUMP</td>\n",
              "      <td>SHEEP</td>\n",
              "      <td>THND_TONNE</td>\n",
              "      <td>A</td>\n",
              "      <td>2024</td>\n",
              "      <td>732.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12136</th>\n",
              "      <td>EU27</td>\n",
              "      <td>MEATCONSUMP</td>\n",
              "      <td>SHEEP</td>\n",
              "      <td>THND_TONNE</td>\n",
              "      <td>A</td>\n",
              "      <td>2025</td>\n",
              "      <td>737.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12137</th>\n",
              "      <td>EU27</td>\n",
              "      <td>MEATCONSUMP</td>\n",
              "      <td>SHEEP</td>\n",
              "      <td>THND_TONNE</td>\n",
              "      <td>A</td>\n",
              "      <td>2026</td>\n",
              "      <td>741.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12138</th>\n",
              "      <td>EU27</td>\n",
              "      <td>MEATCONSUMP</td>\n",
              "      <td>SHEEP</td>\n",
              "      <td>THND_TONNE</td>\n",
              "      <td>A</td>\n",
              "      <td>2027</td>\n",
              "      <td>743.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12139</th>\n",
              "      <td>EU27</td>\n",
              "      <td>MEATCONSUMP</td>\n",
              "      <td>SHEEP</td>\n",
              "      <td>THND_TONNE</td>\n",
              "      <td>A</td>\n",
              "      <td>2028</td>\n",
              "      <td>747.01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>12140 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Локация    Индикатор Тип мяса  Количество Категория   Год  Значение\n",
              "0         AUS  MEATCONSUMP     BEEF      KG_CAP         A  1990      0.00\n",
              "1         AUS  MEATCONSUMP     BEEF      KG_CAP         A  1991     27.81\n",
              "2         AUS  MEATCONSUMP     BEEF      KG_CAP         A  1992     26.28\n",
              "3         AUS  MEATCONSUMP     BEEF      KG_CAP         A  1993     26.24\n",
              "4         AUS  MEATCONSUMP     BEEF      KG_CAP         A  1994     25.54\n",
              "...       ...          ...      ...         ...       ...   ...       ...\n",
              "12135    EU27  MEATCONSUMP    SHEEP  THND_TONNE         A  2024    732.15\n",
              "12136    EU27  MEATCONSUMP    SHEEP  THND_TONNE         A  2025    737.01\n",
              "12137    EU27  MEATCONSUMP    SHEEP  THND_TONNE         A  2026    741.10\n",
              "12138    EU27  MEATCONSUMP    SHEEP  THND_TONNE         A  2027    743.49\n",
              "12139    EU27  MEATCONSUMP    SHEEP  THND_TONNE         A  2028    747.01\n",
              "\n",
              "[12140 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "filename = '/Users/kovalenkov/Documents/Бауманка/3 курс/6 семестр/ТМО/Machine_learning_technologies/meat_consumption.csv'\n",
        "ds = pd.read_csv(filename)\n",
        "\n",
        "pd.set_option('display.max_colwidth', None)\n",
        "pd.set_option('display.float_format', '{:.2f}'.format)\n",
        "ds.columns = ['Локация', 'Индикатор', 'Тип мяса' , 'Количество', 'Категория', 'Год', 'Значение']\n",
        "ds = pd.DataFrame(ds)\n",
        "display(ds)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8RuYUev0m3uK"
      },
      "source": [
        "В датасете не содержится пустых данных.\n",
        "Кодирование категориальных признаков было сделано с помощью Label Encoder.\n",
        "Данный тип кодирования является наиболее часто используемым, преобразование представляет собой однозначное соответствие число <-> уникальное значение категориального признака. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "An0wuitMgjyD",
        "outputId": "1f3d57bc-af77-4ede-c834-8b051fe260ea"
      },
      "outputs": [
        {
          "data": {
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Локация</th>\n",
              "      <th>Индикатор</th>\n",
              "      <th>Тип мяса</th>\n",
              "      <th>Количество</th>\n",
              "      <th>Категория</th>\n",
              "      <th>Год</th>\n",
              "      <th>Значение</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1990</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1991</td>\n",
              "      <td>27.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1992</td>\n",
              "      <td>26.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1993</td>\n",
              "      <td>26.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1994</td>\n",
              "      <td>25.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12135</th>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2024</td>\n",
              "      <td>732.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12136</th>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2025</td>\n",
              "      <td>737.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12137</th>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2026</td>\n",
              "      <td>741.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12138</th>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2027</td>\n",
              "      <td>743.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12139</th>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2028</td>\n",
              "      <td>747.01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>12140 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       Локация  Индикатор  Тип мяса  Количество  Категория   Год  Значение\n",
              "0            1          0         0           0          0  1990      0.00\n",
              "1            1          0         0           0          0  1991     27.81\n",
              "2            1          0         0           0          0  1992     26.28\n",
              "3            1          0         0           0          0  1993     26.24\n",
              "4            1          0         0           0          0  1994     25.54\n",
              "...        ...        ...       ...         ...        ...   ...       ...\n",
              "12135       11          0         3           1          0  2024    732.15\n",
              "12136       11          0         3           1          0  2025    737.01\n",
              "12137       11          0         3           1          0  2026    741.10\n",
              "12138       11          0         3           1          0  2027    743.49\n",
              "12139       11          0         3           1          0  2028    747.01\n",
              "\n",
              "[12140 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "labelencoder = LabelEncoder()\n",
        "ds['Тип мяса'] = labelencoder.fit_transform(ds['Тип мяса'].values)\n",
        "ds['Индикатор'] = labelencoder.fit_transform(ds['Индикатор'].values)\n",
        "ds['Категория'] = labelencoder.fit_transform(ds['Категория'].values)\n",
        "ds['Количество'] = labelencoder.fit_transform(ds['Количество'].values)\n",
        "ds['Локация'] = labelencoder.fit_transform(ds['Локация'].values)\n",
        "display(ds)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K2qL1j09n4Q8"
      },
      "source": [
        "### С использованием метода train_test_split разделите выборку на обучающую и тестовую.\n",
        "Размер тестовой и обучающей выборки 1 к 2."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p5PoQaAcrAZb",
        "outputId": "2c5674e8-1c43-4c69-f1c0-2fd10d57e574"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Обучающая выборка:\n",
            "       Локация  Индикатор  Тип мяса  Количество  Категория   Год  Значение\n",
            "9713        37          0         3           1          0  2020  15301.95\n",
            "8165        27          0         3           1          0  1993     26.91\n",
            "7877         0          0         0           1          0  2017   2507.00\n",
            "9885        38          0         1           1          0  1997    129.95\n",
            "10586       37          0         2           1          0  1996  46260.26\n",
            "...        ...        ...       ...         ...        ...   ...       ...\n",
            "11964       12          0         3           0          0  2009      4.92\n",
            "5191        11          0         1           0          0  2022     35.21\n",
            "5390        25          0         2           0          0  2026     31.53\n",
            "860         24          0         2           0          0  1992     15.30\n",
            "7270        36          0         3           1          0  1995      4.17\n",
            "\n",
            "[8133 rows x 7 columns]\n",
            "Тестовая выборка:\n",
            "       Локация  Индикатор  Тип мяса  Количество  Категория   Год  Значение\n",
            "6104        23          0         2           1          0  1999     36.75\n",
            "10538       21          0         2           1          0  2026   2077.83\n",
            "396         17          0         2           0          0  1996     12.97\n",
            "11146        5          0         2           1          0  2010    124.26\n",
            "7534         2          0         3           1          0  2025    136.13\n",
            "...        ...        ...       ...         ...        ...   ...       ...\n",
            "9920         3          0         3           1          0  1993   2654.93\n",
            "2510        13          0         0           0          0  2016      1.90\n",
            "4870        36          0         1           0          0  2013     28.60\n",
            "2273        10          0         2           0          0  2010      0.59\n",
            "6081        32          0         1           1          0  2015    913.00\n",
            "\n",
            "[4007 rows x 7 columns]\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test= train_test_split(ds,train_size=0.67, random_state=42)\n",
        "\n",
        "print(f\"Обучающая выборка:\\n{X_train}\")\n",
        "print(f\"Тестовая выборка:\\n{X_test}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 732
        },
        "id": "t-JOCzlcfbV2",
        "outputId": "33abd595-f45d-4156-c69f-cb7085f027b3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "       Локация  Индикатор  Тип мяса  Количество  Категория   Год  Значение\n",
            "9713        37          0         3           1          0  2020  15301.95\n",
            "8165        27          0         3           1          0  1993     26.91\n",
            "7877         0          0         0           1          0  2017   2507.00\n",
            "9885        38          0         1           1          0  1997    129.95\n",
            "10586       37          0         2           1          0  1996  46260.26\n",
            "...        ...        ...       ...         ...        ...   ...       ...\n",
            "11964       12          0         3           0          0  2009      4.92\n",
            "5191        11          0         1           0          0  2022     35.21\n",
            "5390        25          0         2           0          0  2026     31.53\n",
            "860         24          0         2           0          0  1992     15.30\n",
            "7270        36          0         3           1          0  1995      4.17\n",
            "\n",
            "[8133 rows x 7 columns]\n",
            "[[0.7  0.3 ]\n",
            " [0.6  0.4 ]\n",
            " [0.45 0.55]\n",
            " ...\n",
            " [0.45 0.55]\n",
            " [0.6  0.4 ]\n",
            " [0.55 0.45]]\n"
          ]
        }
      ],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier \n",
        "predictors = ['Локация', 'Категория'] \n",
        "outcome = 'Количество' \n",
        "print(X_train)\n",
        "\n",
        "new_record = X_train.loc[:, predictors] \n",
        "X = X_train.loc[1:, predictors] \n",
        "y = X_train.loc[1:, outcome] \n",
        "\n",
        "kNN = KNeighborsClassifier(n_neighbors=20) \n",
        "kNN.fit(X, y) \n",
        "kNN.predict(new_record)\n",
        "print(kNN.predict_proba(new_record)) "
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Произведите подбор гиперпараметра K с использованием GridSearchCV и RandomizedSearchCV и кросс-валидации, оцените качество оптимальной модели.\n",
        "Используйте не менее двух стратегий кросс-валидации."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[0.27378305 0.21126761 0.17498764]\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import cross_val_score, cross_validate\n",
        "scores = cross_val_score(KNeighborsClassifier(n_neighbors=2), \n",
        "                         ds, ds['Локация'], cv=3)\n",
        "print(scores)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Значение метрики accuracy для 3 фолдов"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8339377408545651"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.mean(scores)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Усредненное значение метрики accuracy для 3 фолдов"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[0.26692239 0.20562052 0.17863725] 0.21706005353021074\n"
          ]
        }
      ],
      "source": [
        "scores = cross_val_score(KNeighborsClassifier(n_neighbors=2), \n",
        "                         ds, ds['Локация'], cv=3,\n",
        "                        scoring='f1_weighted')\n",
        "print(scores, np.mean(scores))"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Использование метрики f1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'fit_time': array([0.00640798, 0.00781107, 0.00803304]),\n",
              " 'score_time': array([0.10710096, 0.15897202, 0.16736889]),\n",
              " 'test_precision': array([0.33010929, 0.22315103, 0.20495232]),\n",
              " 'train_precision': array([0.85534386, 0.90023732, 0.94270171]),\n",
              " 'test_recall': array([0.27378305, 0.21126761, 0.17498764]),\n",
              " 'train_recall': array([0.8480168 , 0.89682442, 0.94131455]),\n",
              " 'test_f1': array([0.26692239, 0.20562052, 0.17863725]),\n",
              " 'train_f1': array([0.84726898, 0.89641442, 0.94118126])}"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "scoring = {'precision': 'precision_weighted', \n",
        "           'recall': 'recall_weighted',\n",
        "           'f1': 'f1_weighted'}\n",
        "\n",
        "scores = cross_validate(KNeighborsClassifier(n_neighbors=2), \n",
        "                        ds, ds['Локация'], scoring=scoring, \n",
        "                        cv=3, return_train_score=True)\n",
        "scores"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "В отличие от функции cross_val_score, функция cross_validate позволяет использовать для оценки несколько метрик и возращает более детальную информацию."
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "##### Стратегия K-fold"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Данная стратегия работает в соответствии с определением кросс-валидации.\n",
        "\n",
        "Каждой стратегии в scikit-learn ставится в соответствии специальный класс-итератор, который может быть указан в качестве параметра cv функций cross_val_score и cross_validate."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1 2] [0]\n",
            "[0 2] [1]\n",
            "[0 1] [2]\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import KFold\n",
        "X = [\"a\", \"b\", \"c\"]\n",
        "kf = KFold(n_splits=3)\n",
        "for train, test in kf.split(X):\n",
        "    print(\"%s %s\" % (train, test))"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Далее итератор может быть использован в функциях cross_val_score и cross_validate:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[0.15311171 0.13647194 0.10800428 0.13971723 0.12446281]\n"
          ]
        }
      ],
      "source": [
        "kf = KFold(n_splits=5)\n",
        "scores = cross_val_score(KNeighborsClassifier(n_neighbors=2), \n",
        "                         ds, ds['Локация'], scoring='f1_weighted', \n",
        "                         cv=kf)\n",
        "print(scores)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "{'fit_time': array([0.01108813, 0.00729704, 0.01269603, 0.00825405, 0.00961161]),\n",
              " 'score_time': array([0.06818795, 0.06709313, 0.08138776, 0.08531499, 0.08978724]),\n",
              " 'test_precision': array([0.46862436, 0.29006464, 0.21143049, 0.34004254, 0.16902537]),\n",
              " 'train_precision': array([0.87083679, 0.87056821, 0.89995913, 0.92913181, 0.915602  ]),\n",
              " 'test_recall': array([0.11861614, 0.12479407, 0.07825371, 0.11490939, 0.10955519]),\n",
              " 'train_recall': array([0.86655684, 0.86521829, 0.89363674, 0.92637974, 0.9134061 ]),\n",
              " 'test_f1': array([0.15311171, 0.13647194, 0.10800428, 0.13971723, 0.12446281]),\n",
              " 'train_f1': array([0.86588585, 0.86452674, 0.89392474, 0.92631311, 0.91306137])}"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "kf = KFold(n_splits=5)\n",
        "scores = cross_validate(KNeighborsClassifier(n_neighbors=2), \n",
        "                       ds, ds['Локация'], scoring=scoring, \n",
        "                        cv=kf, return_train_score=True)\n",
        "scores"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "##### Leave One Out (LOO)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[ 1  2  3  4  5  6  7  8  9 10 11] [0]\n",
            "[ 0  2  3  4  5  6  7  8  9 10 11] [1]\n",
            "[ 0  1  3  4  5  6  7  8  9 10 11] [2]\n",
            "[ 0  1  2  4  5  6  7  8  9 10 11] [3]\n",
            "[ 0  1  2  3  5  6  7  8  9 10 11] [4]\n",
            "[ 0  1  2  3  4  6  7  8  9 10 11] [5]\n",
            "[ 0  1  2  3  4  5  7  8  9 10 11] [6]\n",
            "[ 0  1  2  3  4  5  6  8  9 10 11] [7]\n",
            "[ 0  1  2  3  4  5  6  7  9 10 11] [8]\n",
            "[ 0  1  2  3  4  5  6  7  8 10 11] [9]\n",
            "[ 0  1  2  3  4  5  6  7  8  9 11] [10]\n",
            "[ 0  1  2  3  4  5  6  7  8  9 10] [11]\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import KFold, RepeatedKFold, LeaveOneOut, LeavePOut, ShuffleSplit, StratifiedKFold\n",
        "X = range(12)\n",
        "kf = LeaveOneOut()\n",
        "for train, test in kf.split(X):\n",
        "    print(\"%s %s\" % (train, test))"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "В тестовую выборку помещается единственный элемент (One Out). Количество фолдов в этом случае определяется автоматически и равняется количеству элементов.\n",
        "\n",
        "Данный метод более ресурсоемкий чем KFold.\n",
        "\n",
        "Существует эмпирическое правило, что вместо Leave One Out лучше использовать KFold на 5 или 10 фолдов."
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Grid Search (решетчатый поиск)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[{'n_neighbors': array([ 5, 10, 15, 20, 25, 30, 35, 40, 45, 50])}]"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "n_range = np.array(range(5,55,5))\n",
        "tuned_parameters = [{'n_neighbors': n_range}]\n",
        "tuned_parameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
              "             param_grid=[{&#x27;n_neighbors&#x27;: array([ 5, 10, 15, 20, 25, 30, 35, 40, 45, 50])}],\n",
              "             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
              "             param_grid=[{&#x27;n_neighbors&#x27;: array([ 5, 10, 15, 20, 25, 30, 35, 40, 45, 50])}],\n",
              "             scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
            ],
            "text/plain": [
              "GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
              "             param_grid=[{'n_neighbors': array([ 5, 10, 15, 20, 25, 30, 35, 40, 45, 50])}],\n",
              "             scoring='accuracy')"
            ]
          },
          "execution_count": 57,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    ds, ds['Локация'], test_size=0.5, random_state=1)\n",
        "\n",
        "clf_gs = GridSearchCV(KNeighborsClassifier(), tuned_parameters, cv=5, scoring='accuracy')\n",
        "clf_gs.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'mean_fit_time': array([0.00628695, 0.00453119, 0.00473919, 0.00434484, 0.00475979,\n",
              "        0.00469732, 0.00469728, 0.00478139, 0.00498343, 0.00439696]),\n",
              " 'std_fit_time': array([0.00101189, 0.00020343, 0.00076725, 0.00019757, 0.00032777,\n",
              "        0.00032818, 0.00045276, 0.00038913, 0.00068028, 0.000266  ]),\n",
              " 'mean_score_time': array([0.04920111, 0.03121157, 0.03213878, 0.03319583, 0.03541555,\n",
              "        0.03615222, 0.0367352 , 0.04176722, 0.03923354, 0.041994  ]),\n",
              " 'std_score_time': array([0.01696428, 0.00068079, 0.00103522, 0.00075006, 0.00184854,\n",
              "        0.00109733, 0.00065719, 0.00571699, 0.00191078, 0.00325244]),\n",
              " 'param_n_neighbors': masked_array(data=[5, 10, 15, 20, 25, 30, 35, 40, 45, 50],\n",
              "              mask=[False, False, False, False, False, False, False, False,\n",
              "                    False, False],\n",
              "        fill_value='?',\n",
              "             dtype=object),\n",
              " 'params': [{'n_neighbors': 5},\n",
              "  {'n_neighbors': 10},\n",
              "  {'n_neighbors': 15},\n",
              "  {'n_neighbors': 20},\n",
              "  {'n_neighbors': 25},\n",
              "  {'n_neighbors': 30},\n",
              "  {'n_neighbors': 35},\n",
              "  {'n_neighbors': 40},\n",
              "  {'n_neighbors': 45},\n",
              "  {'n_neighbors': 50}],\n",
              " 'split0_test_score': array([0.48023064, 0.40444811, 0.36490939, 0.34102142, 0.32784185,\n",
              "        0.31136738, 0.29324547, 0.29324547, 0.29489292, 0.28336079]),\n",
              " 'split1_test_score': array([0.48187809, 0.4291598 , 0.39868204, 0.35667216, 0.33772652,\n",
              "        0.32454695, 0.31630972, 0.30971993, 0.28583196, 0.28995058]),\n",
              " 'split2_test_score': array([0.48105437, 0.41433278, 0.37067545, 0.3369028 , 0.31960461,\n",
              "        0.31136738, 0.30477759, 0.29242175, 0.276771  , 0.27594728]),\n",
              " 'split3_test_score': array([0.49670511, 0.41927512, 0.38714992, 0.37067545, 0.36079077,\n",
              "        0.34349259, 0.33443163, 0.32454695, 0.30971993, 0.30642504]),\n",
              " 'split4_test_score': array([0.45963756, 0.40856672, 0.38056013, 0.35667216, 0.32537068,\n",
              "        0.30889621, 0.29489292, 0.28747941, 0.27759473, 0.26112026]),\n",
              " 'mean_test_score': array([0.47990115, 0.41515651, 0.38039539, 0.3523888 , 0.33426689,\n",
              "        0.3199341 , 0.30873147, 0.3014827 , 0.28896211, 0.28336079]),\n",
              " 'std_test_score': array([0.01181806, 0.00862356, 0.01195279, 0.01216659, 0.01449753,\n",
              "        0.01299918, 0.01526357, 0.01375396, 0.01227762, 0.01499991]),\n",
              " 'rank_test_score': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10], dtype=int32)}"
            ]
          },
          "execution_count": 58,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "clf_gs.cv_results_"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Лучшая модель"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "KNeighborsClassifier()"
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "clf_gs.best_estimator_"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Лучшее значение метрики"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.47990115321252064"
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "clf_gs.best_score_"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Лучшее значение параметров"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'n_neighbors': 5}"
            ]
          },
          "execution_count": 61,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "clf_gs.best_params_"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Изменение качества на тестовой выборке в зависимости от К-соседей"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Matplotlib is building the font cache; this may take a moment.\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x12bfab9d0>]"
            ]
          },
          "execution_count": 63,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(n_range, clf_gs.cv_results_['mean_test_score'])"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "##### Сравните метрики качества исходной и оптимальной моделей."
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Строится зависимость метрики на обучающей выборке от размера выборки.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<module 'matplotlib.pyplot' from '/usr/local/lib/python3.9/site-packages/matplotlib/pyplot.py'>"
            ]
          },
          "execution_count": 68,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn.model_selection import learning_curve, validation_curve\n",
        "def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n",
        "                        n_jobs=None, train_sizes=np.linspace(.1, 1.0, 5), scoring='accuracy'):\n",
        "    plt.figure()\n",
        "    plt.title(title)\n",
        "    if ylim is not None:\n",
        "        plt.ylim(*ylim)\n",
        "    plt.xlabel(\"Training examples\")\n",
        "    plt.ylabel(scoring)\n",
        "    train_sizes, train_scores, test_scores = learning_curve(\n",
        "        estimator, X, y, cv=cv, scoring=scoring, n_jobs=n_jobs, train_sizes=train_sizes)\n",
        "    train_scores_mean = np.mean(train_scores, axis=1)\n",
        "    train_scores_std = np.std(train_scores, axis=1)\n",
        "    test_scores_mean = np.mean(test_scores, axis=1)\n",
        "    test_scores_std = np.std(test_scores, axis=1)\n",
        "    plt.grid()\n",
        "\n",
        "    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
        "                     train_scores_mean + train_scores_std, alpha=0.3,\n",
        "                     color=\"r\")\n",
        "    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
        "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
        "    plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n",
        "             label=\"Training score\")\n",
        "    plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n",
        "             label=\"Cross-validation score\")\n",
        "\n",
        "    plt.legend(loc=\"best\")\n",
        "    return plt\n",
        "\n",
        "plot_learning_curve(KNeighborsClassifier(n_neighbors=5), 'n_neighbors=5', \n",
        "                    X_train, y_train, cv=20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<module 'matplotlib.pyplot' from '/usr/local/lib/python3.9/site-packages/matplotlib/pyplot.py'>"
            ]
          },
          "execution_count": 69,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_learning_curve(KNeighborsClassifier(n_neighbors=5), 'n_neighbors=5', \n",
        "                    X_train, y_train, cv=20, scoring='f1_micro')"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Построение кривой валидации - validation_curve"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<module 'matplotlib.pyplot' from '/usr/local/lib/python3.9/site-packages/matplotlib/pyplot.py'>"
            ]
          },
          "execution_count": 71,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "def plot_validation_curve(estimator, title, X, y, \n",
        "                          param_name, param_range, cv, \n",
        "                          scoring='accuracy'):\n",
        "                                                   \n",
        "    train_scores, test_scores = validation_curve(\n",
        "        estimator, X, y, param_name=param_name, param_range=param_range,\n",
        "        cv=cv, scoring=scoring, n_jobs=1)\n",
        "    train_scores_mean = np.mean(train_scores, axis=1)\n",
        "    train_scores_std = np.std(train_scores, axis=1)\n",
        "    test_scores_mean = np.mean(test_scores, axis=1)\n",
        "    test_scores_std = np.std(test_scores, axis=1)\n",
        "\n",
        "    plt.title(title)\n",
        "    plt.xlabel(param_name)\n",
        "    plt.ylabel(str(scoring))\n",
        "    plt.ylim(0.0, 1.1)\n",
        "    lw = 2\n",
        "    plt.plot(param_range, train_scores_mean, label=\"Training score\",\n",
        "                 color=\"darkorange\", lw=lw)\n",
        "    plt.fill_between(param_range, train_scores_mean - train_scores_std,\n",
        "                     train_scores_mean + train_scores_std, alpha=0.4,\n",
        "                     color=\"darkorange\", lw=lw)\n",
        "    plt.plot(param_range, test_scores_mean, label=\"Cross-validation score\",\n",
        "                 color=\"navy\", lw=lw)\n",
        "    plt.fill_between(param_range, test_scores_mean - test_scores_std,\n",
        "                     test_scores_mean + test_scores_std, alpha=0.2,\n",
        "                     color=\"navy\", lw=lw)\n",
        "    plt.legend(loc=\"best\")\n",
        "    return plt\n",
        "\n",
        "plot_validation_curve(KNeighborsClassifier(), 'knn', \n",
        "                      X_train, y_train, \n",
        "                      param_name='n_neighbors', param_range=n_range, \n",
        "                      cv=20, scoring=\"accuracy\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<module 'matplotlib.pyplot' from '/usr/local/lib/python3.9/site-packages/matplotlib/pyplot.py'>"
            ]
          },
          "execution_count": 73,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "n_range2 = np.array(range(5,125,5))\n",
        "plot_validation_curve(clf_gs.best_estimator_, 'knn', \n",
        "                      ds, ds['Локация'], \n",
        "                      param_name='n_neighbors', param_range=n_range2, \n",
        "                      cv=20, scoring=\"accuracy\")"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
